Biology subjects

Harper, N. S.

Publications and source records attributed to Harper, N. S..

16 recordsLinked to original sources

bioRxiv2026

Predictive dendrites as a foundation for biological learning

A fundamental challenge for neuroscience is to identify laws of synaptic plasticity that are consistent with experimental observations at the level of individual neurons, whilst also explaining the ability of neural networks to learn complex tasks. Here we investigate a candidate law in which neurons adjust synaptic strengths to minimize the mismatch between somatic and dendritic activity. We demonstrate that models based on this rule can effectively adjust synaptic weights across network layers to learn non-linear tasks. Moreover, minimizing mismatch between spiking activity and dendritic inputs leads to integrate-and-fire dynamics and spike-timing-dependent plasticity. The model reproduces the experimentally observed dependencies of plasticity upon initial synaptic weights and spike patterns, whilst also generating a series of testable predictions. Our results show that a single principle - minimization of somato-dendritic mismatch - links learning at the network level with fundamental properties of individual neurons, including membrane potential dynamics and canonical features of synaptic plasticity.

neuroscience↗

bioRxiv2025

Corticofugal gated recurrency captures auditory cortical responses

Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a subtractive form of gated recurrency, consistent with cortical circuitry and feedback that resembles corticofugal projections. This architecture outperformed state-of-the-art models with delay lines or non-biological gated recurrency at predicting auditory cortical responses to natural sounds. Its performance was further improved by incorporating a network layer that mirrors the high neuronal density in superficial cortical layers. Analyzing the model, we found that memory retention is longer in secondary than in primary auditory cortex, and that gated recurrency particularly helps capture responses during abrupt changes and quiet periods in the sounds. This work lays out the fundamental functional circuitry of the auditory pathway for processing the sequential dependencies in natural sounds.

neuroscience↗

bioRxiv2025

Hierarchical recurrent temporal prediction as a model of the mammalian dorsal visual pathway

A major goal of neuroscience is to identify whether there are generalized principles that can explain the diverse structures and functions of the brain. The principle of temporal prediction provides one approach, arguing that the sensory brain is optimized to represent stimulus features that efficiently predict the immediate future input. Previous work has demonstrated that feedforward hierarchical temporal prediction models can capture the tuning properties of neurons along the visual pathway, and that recurrent temporal prediction models can explain local functional connectivity within primary visual cortex. However, the visual system is also characterized by extensive inter-areal feedback recurrency, which existing models lack. We aimed to better account for the dynamic features of neurons in the visual cortex by incorporating both local recurrency and inter-areal feedback connectivity into a hierarchical temporal prediction model. The resulting model captured tuning for pattern motion, surround suppression and elements of inter-areal functional connectivity in visual cortex. Moreover, compared with several alternative normative models, the hierarchical recurrent temporal prediction model provided the closest fit to these tuning properties and was best able to explain the emergence of neuronal response properties across the visual cortex. Accordingly, temporal prediction can account for information processing throughout the visual pathway. Author summaryTask-driven artificial neural networks have provided valuable insights into the computational operations performed by neurons in the brain. In particular, models trained on various normative principles have been shown to capture some of the feature selectivity of neurons in the visual system. However, these models generally ignore the substantial role of inter-areal feedback connectivity in neural function. This study presents an alternative approach to understanding the neural computations underlying the functional organization of the visual system, by providing compelling evidence that networks trained for temporal prediction can explain the emergence of higher-level response properties across the visual cortex and relating these features to both local and inter-areal feedback connectivity.

neuroscience↗

bioRxiv2024

Prediction of future input explains lateral connectivity in primary visual cortex

Neurons in primary visual cortex (V1) show a remarkable functional specificity in their pre- and postsynaptic partners. Recent work has revealed a variety of wiring biases describing how the short- and long-range connections of V1 neurons relate to their tuning properties. However, it is less clear whether these connectivity rules are based on some underlying principle of cortical organization. Here, we show that the functional specificity of V1 connections emerges naturally in a recurrent neural network optimized to predict upcoming sensory inputs for natural visual stimuli. This temporal prediction model reproduces the complex relationships between the connectivity of V1 neurons and their orientation and direction preferences, the tendency of highly connected neurons to respond more similarly to natural movies, and differences in the functional connectivity of excitatory and inhibitory V1 populations. Together, these findings provide a principled explanation for the functional and anatomical properties of early sensory cortex.

neuroscience↗

bioRxiv2024

Temporal prediction captures key differences between spiking excitatory and inhibitory V1 neurons

Neurons in primary visual cortex (V1) respond to natural scenes with a sparse and irregular spike code that is carefully balanced by an interplay between excitatory and inhibitory neurons. These neuron classes differ in their spike statistics, tuning preferences, connectivity statistics and temporal dynamics. To date, no single computational principle has been able to account for these properties. We developed a recurrently connected spiking network of excitatory and inhibitory units trained for efficient temporal prediction of natural movie clips. We found that the model exhibited simple and complex cell-like tuning, V1-like spike statistics, and, notably, also captured key differences between excitatory and inhibitory V1 neurons. This suggests that these properties collectively serve to facilitate efficient prediction of the sensory future.

neuroscience↗

bioRxiv2024

Temporal prediction captures retinal spiking responses across animal species

The retinas role in visual processing has been viewed as two extremes: an efficient compressor of incoming visual stimuli akin to a camera, or as a predictor of future stimuli. Addressing this dichotomy, we developed a biologically-detailed spiking retinal model trained on natural movies under metabolic-like constraints to either encode the present or to predict future scenes. Our findings reveal that when optimized for efficient prediction[~] 100 ms into the future, the model not only captures retina-like receptive fields and their mosaic-like organizations, but also exhibits complex retinal processes such as latency coding, motion anticipation, differential tuning, and stimulus-omission responses. Notably, the predictive model also more accurately predicts the way retinal ganglion cells respond across different animal species to natural images and movies. Our findings demonstrate that the retina is not merely a compressor of visual input, but rather is fundamentally organized to provide the brain with foresight into the visual world.

neuroscience↗

bioRxiv2023

Cochlear tuning characteristics arise from temporal prediction of natural sounds

The cochlea decomposes incoming sound waveforms into different frequency components along the length of its basilar membrane. The receptor hair cells at the apical end of this resonant membrane are tuned to the lowest sound frequencies, with the preferred sound frequency of hair cell tuning increasing near-exponentially along the length of the membrane towards its basal end. This frequency composition of the sound is then transmitted to the brain by the auditory nerve fibers that innervate the inner hair cells. Hair cells respond to a sound impulse with a temporally asymmetric envelope and the sharpness of their tuning changes as the frequency to which they are most sensitive varies with their position along the basilar membrane. We ask if there is a normative explanation for why the cochlea decomposes sounds in this manner. Inspired by findings in the retina, we propose that cochlear tuning properties may be optimized for temporal prediction. This principle states that the sensory features represented by neurons are optimized to predict immediate future input from recent past input. We show that an artificial neural network optimized for temporal prediction of the immediate future of raw waveforms of natural sounds from their recent past produces tuning properties that resemble those observed in the auditory nerve. Specifically, the model captures the temporally asymmetric impulse responses, the tonotopic distribution and variation in tuning sharpness along the cochlea, and the frequency glide polarity of the impulse responses. These characteristics are not captured by a similar model optimized for compression of the sound waveform, rather than prediction. Given its success in accounting for the tuning properties at various processing levels in the auditory and visual systems, this finding for the cochlea provides further evidence that temporal prediction may be a general principle of sensory processing.

neuroscience↗

bioRxiv2023

Dissociable roles of the auditory midbrain and cortex in processing the statistical features of natural sound textures

It is well established that sound texture perception takes advantage of a hierarchy of time-averaged statistical features of acoustic stimuli, but much remains unclear about how these statistical features are processed in auditory subcortical and cortical regions. Here, we compared the neural representation of sound textures in the inferior colliculus (IC), and primary and non-primary auditory cortex (AC) of anesthetized rats with texture morph stimuli which gradually add statistical features of increasingly higher order. We generated texture morphs for a representative subset of 13 sound textures, chosen to span the three principal component dimensions of a corpus of 200 natural texture recordings. For each of the 13 texture types, six different exemplar morphs were synthesized using different random seeds. All exemplars of each texture type have the same long term statistics, but they differ in their acoustic waveforms. Meanwhile, different texture types differ in statistical features as well as waveforms. An analysis of transient and ongoing multi-unit responses to our stimulus set showed that the IC units were sensitive to every type of statistical feature, albeit to a varying extent, but only a small proportion of AC units were overtly sensitive to any statistical features. Differences in texture types explained more of the variance of IC neural responses than did differences in exemplars, indicating a degree of "texture-type tuning" in the IC, but this was, perhaps surprisingly, not the case for AC responses. We also evaluated the accuracy of texture type classification from single-trial population activity and found that IC responses became more informative as more summary statistics were included in the texture morphs, while for AC population responses classification performance remained constant, and consistently lower, than for the IC. These results argue against the idea that AC plays an important role in encoding statistical features of natural sound textures.

physiology↗

bioRxiv2022

Omission responses in field potentials but not spikes in rat auditory cortex

Non-invasive recordings of gross neural activity in humans often show responses to omitted stimuli in steady trains of identical stimuli. This has been taken as evidence for the neural coding of prediction or prediction error. However, evidence for such omission responses from invasive recordings of cellular-scale responses in animal models is scarce. Here, we sought to characterise omission responses using extracellular recordings in the auditory cortex of anaesthetised rats. We profiled omission responses across local field potentials (LFP), analogue multiunit activity (AMUA), and single/multi-unit spiking activity, using stimuli that were fixed-rate trains of acoustic noise bursts where 5% of bursts were randomly omitted. Significant omission responses were observed in LFP and AMUA signals, but not in spiking activity. These omission responses had a lower amplitude and longer latency than burst-evoked sensory responses, and omission response amplitude increased as a function of the number of preceding bursts. Contrary to theories of neural entrainment, rhythmic stimulus presentation did not increase low-frequency phase-locking of neural activity specific to the stimulus presentation rate. Together, our findings show that omission responses are observed in LFP and AMUA signals, with laminar specificity, but are not observed in spiking activity, and do not show evidence for low-frequency phase locking. This has implications for models of cortical processing that require many neurons to encode prediction error in their spike output, and may have some consistency with representation of error in dendrites electrotonically distant from the soma.

neuroscience↗

bioRxiv2021

Cortical adaptation to sound reverberation

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.

neuroscience↗

bioRxiv2020

Exploring the distribution of statistical feature parameters for natural sound textures

Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". Recent psychoacoustic study using sound textures by [1] reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the cochlear envelope. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 66% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 90% of the variance in over 1000 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.

neuroscience↗

bioRxiv2019

Simple spectral transformations capture the contribution of peripheral processing to cortical responses to natural sounds

Processing in the sensory periphery involves various mechanisms that enable the detection and discrimination of sensory information. Despite their biological complexity, could these processing steps sub-serve a relatively simple transformation of sensory inputs, which are then transmitted to the CNS? Here we explored both biologically-detailed and very simple models of the auditory periphery to find the appropriate input to a phenomenological model of auditory cortical responses to natural sounds. We examined a range of cochlear models, from those involving detailed biophysical characteristics of the cochlea and auditory nerve to very pared-down spectrogram-like approximations of the information processing in these structures. We tested the capacity of these models to predict the time-course of single-unit neural responses recorded in the ferret primary auditory cortex, when combined with a linear non-linear encoding model. We show that a simple model based on a log-spaced, log-scaled power spectrogram with Hill-function compression performs as well as biophysically-detailed models of the cochlea and the auditory nerve. These findings emphasize the value of using appropriate simple models of the periphery when building encoding models of sensory processing in the brain, and imply that the complex properties of the auditory periphery may together result in a simpler than expected functional transformation of the inputs.

neuroscience↗

bioRxiv2019

Hierarchical temporal prediction captures motion processing from retina to higher visual cortex

Visual neurons respond selectively to specific features that become increasingly complex in their form and dynamics from the eyes to the cortex. Retinal neurons prefer localized flashing spots of light, primary visual cortical (V1) neurons moving bars, and those in higher cortical areas, such as middle temporal (MT) cortex, favor complex features like moving textures. Whether there are general computational principles behind this diversity of response properties remains unclear. To date, no single normative model has been able to account for the hierarchy of tuning to dynamic inputs along the visual pathway. Here we show that hierarchical application of temporal prediction - representing features that efficiently predict future sensory input from past sensory input - can explain how neuronal tuning properties, particularly those relating to motion, change from retina to higher visual cortex. This suggests that the brain may not have evolved to efficiently represent all incoming information, as implied by some leading theories. Instead, the selective representation of sensory inputs that help in predicting the future may be a general neural coding principle, which when applied hierarchically extracts temporally-structured features that depend on increasingly high-level statistics of the sensory input.

neuroscience↗

bioRxiv2018

Beat-Relevant Signals in Auditory Cortical Responses to Musical Excerpts

Musical beat perception is widely regarded as a high-level ability involving widespread coordination across brain areas, but how low-level auditory processing must necessarily shape these dynamics, and therefore perception, remains unexplored. Previous cross-species work suggested that beat perception in simple rhythmic noise bursts is shaped by neural transients in the ascending sensory pathway. Here, we found that low-level processes even substantially explain the emergence of beat in real music. Firing rates in the rat auditory cortex in response to twenty musical excerpts were on average higher on the beat than off the beat tapped by human listeners. This \"neural emphasis\" distinguished the perceived beat from alternative interpretations, was predictive of the degree of consensus across listeners, and was accounted for by a spectrotemporal receptive field model. These findings indicate that low-level auditory processing may have a stronger influence on the location and clarity of the beat in music than previously thought.

neuroscience↗

bioRxiv2018

Exponentially-decaying temporal integration in a network model can explain much of the neural sensitivity to stimulus history in primary auditory cortex.

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each units response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.\n\nAUTHOR SUMMARYThe responses of neurons in the primary auditory cortex depend on the recent history of sounds over seconds or less. Typically, this dependence on the past has been modelled by applying a wide span of time delays to the input, although this is likely to be biologically unrealistic. Real neurons integrate the history of their activity due to the dynamical properties of their cell membranes and other components. We show that a network with a realistically narrow span of delays and with units having dynamic characteristics like those found in neurons, succinctly models neural responses recorded from ferret primary auditory cortex. Because these integrative properties are widespread, our dynamic network provides a basis for modelling responses in other neural systems.

neuroscience↗

bioRxiv2017

Sensory cortex is optimised for prediction of future input

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent past of sensory input that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, in their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields tended to resemble those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.\n\nImpact statementPrediction of future input explains diverse neural tuning properties in sensory cortex.

neuroscience↗